Convert scientific papers into executable code repositories with reproducibility workflows.
npx skills add https://github.com/qosi-org/arxivist --skill arxivist
Please help me install this Agent Skill. Skill: arxivist Source: https://github.com/qosi-org/arxivist/tree/main/skill Command: npx skills add https://github.com/qosi-org/arxivist --skill arxivist
ArXivist removes the manual work of turning scientific papers into working code by extracting a paper’s architecture, equations, training details, and evaluation protocol into a reproducible implementation plan.
Ask ArXivist to turn the attached research paper into a reproducible code repository and notebook.
High-intent search queries and answers about installing and using this skill.
Converting an arXiv paper to code requires parsing the PDF or URL to extract architecture, equations, and training details into a reproducible implementation plan. The workflow generates an executable repository and a notebook for quick testing and validation.
Yes, generating a reproducible notebook from a scientific PDF is possible by uploading the document for structured extraction. The system parses evaluation protocols and training details, creating an executable notebook designed for quick testing and result validation.
Reproducing machine learning research papers is best handled by tracking implementation assumptions, ambiguities, and confidence levels. This ensures gaps are explicit rather than hidden, providing a reproducible code repository alongside a comparison report against experimental results.
Yes, the paper-to-code workflow supports DOIs, uploaded PDFs, arXiv URLs, and arXiv PDFs. It applies multi-stage orchestration to parse the input, store registry data, plan the architecture, and generate the final executable codebase.
Generated code may fail to match original evaluation metrics due to hidden implementation gaps. The reproducibility workflow explicitly tracks assumptions and ambiguities extracted from the paper, producing a comparison artifact to identify where your experimental results diverge.
Multi-agent orchestration for scientific paper implementation coordinates parsing, registry storage, architecture planning, and code generation. It systematically extracts equations and training details, orchestrating these stages to output a fully executable, reproducible repository.
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